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An enterprise might choose to replicate its data for several reasons:



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data, and can be explained readily to a non-networking expert These developments have helped immensely in demystifying fractal-based traf c modeling and have given rise to new insights and physical understanding of the effects of large-time scaling properties in measured network traf c on the design, management, and performance of high-speed networks However, to provide a complete description of data network traf c, the same kind of understanding is necessary with respect to the dynamic nature of traf c over small time scales, from a few hundreds of milliseconds downward.





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Imagine my delight to discover that I scored a 99.5 on Twittergrader.com (Figure 3.5). Twitter Grader thinks that my measly efforts on Twitter are better than 4,721,957 others they are tracking. Only a half a percent of those trackees scored higher than me. It s so tempting to wrestle with a score like that and see what it takes to push it up to 99.6 and then 99.7. But how does that make me a better businessperson It s a mystery.

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Probably the most popular use of replication is for disaster recovery. Sending data over a wide area network to a remote site means that the data will be protected in the event that something happens to the facility that contains the master server. It is this use of replication that leads us to place replication so high on the Availability Index. Other uses are valuable, but are not as critical to achieving high availability. Wide area replication, in fact, enables (but does not define) the 10th and final level of the Index, disaster recovery. Data can be replicated from one system to another within the same facility. In this model, the destination system can work on and with the replicated data independently of the master. The destination system might be used to generate reports, mine data, or perform tape backups, all without affecting the original copy of the data, or impacting the CPU, and therefore the users, of the master. Some FMS packages use replication to share their data. As we said in 15, Local Clustering and Failover, even though some vendors recommend it, we maintain that data sharing via replication is inferior to data sharing via shared disks.

Because of the predominant protocols and end-to-end congestion control mechanisms that play a central role in modern-day data networks and determine the ow of packets over those ne time scales and at the different layers in the TCP=IP protocol hierarchy, studying the ne-time scale behavior or local characteristics of data traf c is intimately related to understanding the complex interactions that exist in data networks such as the Internet between the different connections, across the different layers in the protocol hierarchy, over time as well as in space.

Replication types or styles can be categorized in two ways:

In this chapter, we rst summarize the results that provide a unifying and consistent picture of the large-time scaling behavior of data traf c and discuss the appropriateness of self-similar processes such as fractional Gaussian noise for modeling the uctuations of the traf c rate process around its mean and for providing a complete description of the traf c on individual links within the network Then we report on recent progress in studying the small-time scaling behavior in data network traf c and outline a number of challenging open problems that stand in the way of providing an understanding of the local traf c characteristics that is as plausible, intuitive, appealing, and relevant as the one that has been found for the global or large-time scaling properties of data traf c..

20.2 THE LARGE-TIME SCALING BEHAVIOR OF NETWORK TRAFFIC In this section, we demonstrate why the empirically observed large-time scaling behavior or (asymptotic) self-similarity of aggregate network traf c is an additive property, with the additional requirement that the individual component processes that generate the total traf c exhibit certain high-variability or heavy-tailed characteristics.

By the amount of latency they introduce (none, a little, or a lot) By the entity that manages and initiates the replication (hardware, software, filesystem, or an application)

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